The broad objective of staple yarn manufacturing processes is to convert fibers into yarn. Fibers are, in the most fundamental terms, the materials of construction for yarn. Textile processing machines operate on fibers, in a multiplicity of steps, to prepare them for the final step, conversion into yarn. Within each step there are usually many machines of the same type, as indicated by the numbers in parentheses in FIG. 25. These lists describe typical rotor or ring spinning plants producing 300,000 pounds/week of 25 tex, 100% cotton yarn.
Staple yarn manufacturing processes thus begin with bales of staple fiber and end with yarn. Next comes fabric formation, usually by weaving or knitting means. It is readily apparent that fabric production characteristics and quality parameters depend on yarn quality parameters. Strong and uniform yarns "run better" and are more pleasing to the consumer. Yarn quality parameters depend upon fiber quality parameters and upon machinery performance parameters. Some of these parameters are listed in FIG. 26. Importantly, operating profit for yarn manufacturing depends as much or more on selection of raw materials and machinery operation, jointly optimized, as it does on selling price.
For nearly 50 years, yarn parameter measurement instruments have been available, first for operation in Quality Control (QC) labs and, for about 25 years, for continuously monitoring yarn parameters on certain manufacturing machines. Zellweger Uster, Uster, Switzerland manufactures TENSORAPID and TENSOJET, which measure yarn strength/elongation and Uster Tester 3, which measures yarn evenness or uniformity, hairiness, and fineness (linear density). Some of these QC laboratory instruments have on-line machinery monitoring counterparts. Rieter Machine Works, Ingolstadt, Germany, manufactures OPTRA, a new laboratory instrument which measures trash in yarn.
For fiber parameter measurement, there are increasingly available, over the past 15 years, modern fiber testing instruments which provide multiple data products, are known in the art as High Volume Instruments (HVI), and are manufactured by Zellweger Uster, Knoxville, Tennessee. The primary data products now in use are strength, length, micronaire, color, and trash.
HVI measures fiber properties in the bale state only. Yarn testing equipment measures fiber in the yarn state only. The few on-line instruments in practice measure sliver or yarn uniformity only. The number of measured parameters and machines which are monitored needs to increase dramatically; competitive forces assure that this will happen, especially now that we are in the "Information Age."
Fiber information from HVI and yarn information from TensoRapid have been used to improve performance and profitability of the yarn manufacturing process. These methods are known in the textile industry as, for examples, engineered fiber selection (EFS) or bale information and analysis system (BIAS). EFS software was developed by Cotton Incorporated, Raleigh, N.C., and is sold to textile mills who use primarily United States cotton. BIAS was developed and is sold by Zellweger Uster, Knoxville, Tenn. and Uster, Switzerland. The EFS and BIAS software enable the user to select bales of raw material which can result in better raw materials utilization, fewer processing problems, improved yarn properties, and more profit.
Under carefully controlled conditions, the coefficients of determination (R.sup.2) for these multiple linear regression statistical methods relating bale and yarn properties can be as good as R.sup.2 .about. 0.9. This means, under these best of circumstances, that 90% of the variability in the output yarn can be explained by variability in the input fiber properties. More typical results are R.sup.2 .about. 50%, which means that the variations in input fiber properties (the bale state) can only explain 50% of the variations of fiber in the output state, yarn. This means that the 50% which is unexplained is primarily attributable to the variations associated with the processing machinery.
Input-output relationships for EFS and BIAS are based on multiple linear regression statistical techniques which are well known in the art. The output yarn property is typically the single parameter of yarn tenacity or strength. Relationships for other properties are needed.
Since the correlations are established between bale state and yarn state, then, evidently, another limitation of this predictive methodology is that no information is provided on the large number of important intermediate processing steps.
Another problematic feature of this linear regression type statistical approach is that it requires fixed machinery settings and constant production environments. It is a major undesirable consequence that these techniques disguise the influence of the multiple steps of interconnected machinery and their various complicated interactions.
Further, current HVI methods provide average measurements on small bundles of fiber or on the surfaces of fiber masses. Distributions of single entities, fibers or imperfections (neps of several types, trash, bark, grass, etc.), cannot be measured.
Still further, no information is provided on certain important fiber parameters such as trash counts/gram, short fiber content, or neps, etc. These parameters also relate to processing problems, yarn quality, and profit.
The Advanced Fiber Information System, AFIS, was invented in the mid-1980s to provide some of this missing information. AFIS provides distributions of single entity measurements on all fiber states, from bolls on the cotton plant itself to the final fiber state just prior to spinning (steps 7 and 6 in the examples in FIG. 25). Recent and relevant patent and open literature citations are listed in the references. AFIS currently operates in the QC Laboratory, frequently beside yarn test equipment; the next step is on-line monitoring of AFIS measurement parameters. MANTIS is another new instrument which measures single fiber breaking tension, elongation, and diameter and is also described in the references.
AFIS and MANTIS can measure all of the fiber properties of interest in the mill from the bale through the final preparation stage to spinning. This increasingly available new fiber information, in concert with widely available yarn information, has already deepened the understanding of the fiber to yarn engineering process and has improved its quality and profitability in leading mills, worldwide. However, this process has only now begun. There is a growing need for organizing methodology to avoid information overload and according to which better use of the rapidly-increasing fiber and yarn information can be made.
Ultimately, optimal control of the textile manufacturing processes needs to be realized, preferably in real time, from on-line measurements. This makes the need for organizing methodology urgent. Importantly, such methodology is the only practical way to achieve the deepest understandings of the fiber-to-yarn engineering process upon which successful optimal control strategies can be developed.
Conceptualization and development of organizing methodology is not easy. The textile manufacturing process has several unique features which cause the fiber-to-yarn engineering process, and thus optimal control, to be difficult. Among the important features differentiating textile manufacturing processes from others is variability: their input-output relationships are widely diverse and have large random components. That is, the variation from machine to machine or even of the parameters within the raw and processed material are very large. These large random components, relative to deterministic or fixed components, must be recognized as part of the measurement and control problem.
Also, with few exceptions, the operating parameters ("settings") of textile processing machinery have not been made easily variable. It was mentioned above that the machines were assumed to be constant for linear regression predictions. Constant performance has indeed been the objective of machinery manufacturers for nearly a century. One of our points of departure from the current machine design and operation is the recognition that most textile processing machinery can, with redesign, easily be changed dynamically. Some operating parameters can be changed very rapidly, in less than 1 second, for example. This could accommodate the ever present and large random variations of the input fiber material. Being able to adapt to input variations and to produce a more constant output at the same or lower costs (i.e. optimal control) would be a very desirable result not now possible. Availability of optimal control signal information, as described below, provides the necessary incentives to change machinery manufacturers' and mill owners' thinking. This will be appreciated as a major technological innovation.
One can now better appreciate the importance and complexity of the process control problem in yarn manufacturing. That is, given available fiber, with known properties and costs, and, target yarn properties one seeks to determine the settings for which processing performance is "optimal". What constitutes "optimal"? Do we want to manufacture the cleanest yarn? The strongest yarn? The most even yarn? Or do we want to maximize profit? "All of the above" is not a satisfactory answer; most of these objectives are usually in conflict with the others.
Evidently, the purpose of this logical development and the resulting rhetorical questions are to dramatize the fundamentally important and dominating fact that, in all practical cases, over the long term, it is profit that must be maximized. This means that there must be further relationships describing the selling price, or profit, or "benefit" or "value" derived from operating with certain input/output relationships, such as affected by the raw material or by the machinery settings. Stated simplistically and dramatically: it would be most desirable to produce superior yarn while operating the machinery at minimal capital and operating costs and while using less expensive fiber to, thereby, yield increased profit.
Thus the optimization problem is more complex than optimizing physical parameters. Appropriate consideration must also be given to market conditions, to the total capital and operating costs of the plant, even to personnel involved in the manufacturing process. Thus, optimal control means, in the context of this disclosure, maximizing profit subject to constraints imposed by the materials, machinery, and yarn.
Whereas the optimal control problem is large in scope, there are now available powerful technologies to solve it. The rapid development of digital computation means and of advanced statistical analysis means provide good tools. What is needed is a general, practical methodology to organize these tools and modern measurements of fiber and yarn properties into useful methods.